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Hardware-aware Neural Architecture Search (HW-NAS) is a technique used toautomatically design the architecture of a neural network for a specific taskand target hardware. However, evaluating the performance of candidatearchitectures is a key challenge in HW-NAS, as it requires significantcomputational resources. To address this challenge, we propose an efficienthardware-aware evolution-based NAS approach called HW-EvRSNAS. Our approachre-frames the neural architecture search problem as finding an architecturewith performance similar to that of a reference model for a target hardware,while adhering to a cost constraint for that hardware. This is achieved througha representation similarity metric known as Representation Mutual Information(RMI) employed as a proxy performance evaluator. It measures the mutualinformation between the hidden layer representations of a reference model andthose of sampled architectures using a single training batch. We also use apenalty term that penalizes the search process in proportion to how far anarchitecture's hardware cost is from the desired hardware cost threshold. Thisresulted in a significantly reduced search time compared to the literature thatreached up to 8000x speedups resulting in lower CO2 emissions. The proposedapproach is evaluated on two different search spaces while using lowercomputational resources. Furthermore, our approach is thoroughly examined onsix different edge devices under various hardware cost constraints.

Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric